Visual Data Simulation for Deep Learning in Robot Manipulation Tasks

Surák, Miroslav; Košnar, Karel; Kulich, Miroslav; Kozák, Viktor; Přeučil, Libor

doi:10.1007/978-3-030-14984-0_29

Cited by 3 publications

(2 citation statements)

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“…This resulted in a dataset with close to 12,000 labeled images. The labeled images were later used in [ 36 ] for visual data simulation for deep learning, in which we explored the possibility of using synthetic data for the developed detection and localization system. The system trained on synthetic data was later successfully tested with comparable results to the original system trained on real images.…”

Section: Resultsmentioning

confidence: 99%

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Data-Driven Object Pose Estimation in a Practical Bin-Picking Application

Kozák

Sushkov

Kulich

et al. 2021

Sensors

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This paper addresses the problem of pose estimation from 2D images for textureless industrial metallic parts for a semistructured bin-picking task. The appearance of metallic reflective parts is highly dependent on the camera viewing direction, as well as the distribution of light on the object, making conventional vision-based methods unsuitable for the task. We propose a solution using direct light at a fixed position to the camera, mounted directly on the robot’s gripper, that allows us to take advantage of the reflective properties of the manipulated object. We propose a data-driven approach based on convolutional neural networks (CNN), without the need for a hard-coded geometry of the manipulated object. The solution was modified for an industrial application and extensively tested in a real factory. Our solution uses a cheap 2D camera and allows for a semi-automatic data-gathering process on-site.

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Section: Resultsmentioning

confidence: 99%

“…Provided that the camera and lighting systems remain unchanged, the pose estimation method can be transferred without retraining. Additionally, our previous work proved that the system can also be used with synthetic training data [ 36 ].…”

Section: Discussionmentioning

confidence: 99%